Outcome creation based upon synthesis of history

ABSTRACT

A method of exercising effective influence over future occurrences using knowledge synthesis is described. Techniques include influencing methods that yield actions, once a proposed outcome has been assumed. This is different from methods, typically referred to as “predictive” or “prescriptive” that use analytics to model future results based upon existing data and predict most likely outcome. One or more methods of analysis of historical data, in a hierarchical manner, determine events which led to an observed outcome. The outcome-based algorithms use, as input, a future event or state and generate attributes that are necessary precursors. By creating these attributes, the future can be affected. Where necessary, synthetic contributors of such attributes are also created and made to act in ways consistent with generating the assumed outcome. These contributors might be called upon respectively, to post favorable opinions, to report balmy weather, or to describe sales to a certain population demographic.

BACKGROUND Prior Application

This application is a priority application.

Technical Field

The system, apparatuses and methods described herein generally relate tomachine learning techniques, and, in particular, to creating desiredoutcomes using predictive analytics and the synthesis of events.

Description of the Related Art

Machine learning and artificial intelligence algorithms are seen in thecomputer science literature for the past half century, with slowprogress seen in the predictive analytics realm. We can now take a largedata set of various features, and process that learning data set throughone of a number of learning algorithms to create a rule set based on thedata. This rule set can reliably predict what will occur for a givenevent. For instance, in a fraud prediction application, with a givenevent (set of attributes), the algorithm can determine if thetransaction is likely to be fraudulent.

Machine learning is a method of analyzing information using algorithmsand statistical models to find trends and patterns. In a machinelearning solution, statistical models are created, or trained usinghistorical data. During this process, a sample set of data is loadedinto the machine learning solution. The solution then findsrelationships in the training data. As a result, an algorithm isdeveloped that can be used to make predictions about the future. Next,the algorithm goes through a tuning process. The tuning processdetermines how an algorithm behaves in order to deliver the bestpossible analysis. Typically, several versions, or iterations of a modelare created in order to identify the model that delivers that mostaccurate outcomes.

Generally, models are used to either make predictions about the futurebased on past data, or discover patterns in existing data. When makingpredictions about the future, models are used to analyze a specificproperty or characteristic. In machine learning, these properties orcharacteristics are known as features. A feature is similar to a columnin a spreadsheet. When discovering patterns, a model could be used toidentify data that is outside of a norm. For example, in a data setcontaining payments sent from a bank, a model could be used to identifyunusual payment activity that may indicate fraud.

Once a model is trained and tuned, it is typically published or deployedto a production or QA environment. In this environment, data is oftensent from another application in real-time to the machine learningsolution. The machine learning solution then analyzes the new data,compares it to the statistical model, and makes predictions andobservations. This information is then sent back to the originatingapplication. The application can use the information to perform avariety of functions, such as alerting a user to perform an action,displaying data that falls outside of a norm, or prompting a user toverify that data was properly characterized. The model learns from eachintervention and becomes more efficient and precise as it recognizespatterns and discovers anomalies.

An effective machine learning engine can automate development of machinelearning models, greatly reducing the amount of time spent reviewingfalse positives, call attention to the most important items, andmaximizes performance based on real-world feedback.

However, machine learning techniques look to the past to predict thefuture. They are passive algorithms, incapable of creating an action.What if, given a learning data set, one wanted to create a certainoutcome? Present teachings on machine learning fail to disclose how touse a machine learning data set to create a desired outcome.

BRIEF SUMMARY OF THE INVENTION

A method for creating a desired outcome is described herein. The methodis made up of the steps of (1) inputting the desired outcome on acomputer; (2) sending the desired outcome to a machine learning serverover a network; (3) parsing rules in a machine learning model todetermine a set of necessary-past attributes for creating the desiredoutcome, on the machine learning server; (4) filtering the set ofnecessary-past attributes through a list of synthetic features to createsynthetic contributors; (5) determining the synthetic contributorsrequired to create the desired outcome; and (6) outputting the syntheticcontributors.

In some embodiments, the method also includes the step of creating themachine learning model by operating a training module on a machinelearning database. The method could also include the step of creatingthe desired outcome by automatically taking action to implement thesynthetic contributors. In some cases, the desired outcome relates tobanking. And the synthetic contributors could include information aboutbank accounts. In some embodiments the synthetic contributors are outputto the computer and in others the synthetic contributors are output tosoftware on the machine learning server. The parsing of the rules couldinclude reverse engineering of the machine learning model. The list ofsynthetic features could be machine generated. The method could alsoinclude the step of locating the desired outcome in a set of machinelearning data and creating a dataset for the machine learning model withdata proximate to the desired outcome.

A device for creating a desired outcome is also described in thisdocument. The device is made up of a machine learning databaseelectrically connected to a special purpose machine learning server,where the special purpose machine learning server has a list ofsynthetic features. The special purpose machine learning server acceptsan input of the desired outcome, and sends the desired outcome to anoutcome creation engine. The outcome creation engine parses the rules ofa machine learning model to derive a set of necessary-past attributes.The set of necessary-past attributes are filtered through the list ofsynthetic features to determine synthetic contributors required tocreate the desired outcome.

The machine learning model could be created by operating a trainingmodule on the machine learning database. In some embodiments, thedesired outcome is created by automatically taking action to implementthe synthetic contributors. In some cases, the desired outcome relatesto banking. And the synthetic contributors could include informationabout bank accounts. In some embodiments the synthetic contributors areoutput to the computer and in others the synthetic contributors areoutput to software on the machine learning server. In some cases, therules of the machine learning model are reverse engineered to derive theset of necessary-past attributes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the component for outputting the attributesrequired for creating the desired outcome.

FIG. 2 is a block diagram showing the loop of the environment isimpacted by the creations of the desired outcome.

FIG. 3 is a diagram of one possible hardware environment for operatingthe outcome creation engine.

FIG. 4 is a flow chart of the outcome creation process.

FIG. 5 is a chart of a banking example of finding a necessary past.

DETAILED DESCRIPTION

The inventions herein relate to exercising effective influence overfuture occurrences using knowledge synthesis. Techniques have beeninvented and combined with certain others to achieve innovativeinfluencing methods that yield actions 206, once a proposed outcome 106has been assumed. This is different from methods, typically referred toas “predictive” or “prescriptive” that use analytics to model futureresults 103 based upon existing data 102 and predict most likely outcome105 based upon natural or planned actions: “if-then” results. Theinventions use one or more established methods of analysis of historicaldata 102 to determine, in a hierarchical manner, the events which led toan observed outcome. The outcome based algorithms 107 then use, asinput, a cast future event or state 106 and generate a set of attributes108, 202 that are necessary precursors. By creating these attributes202, or the appearance of their creation, the future can be affected.Where necessary, synthetic contributors 205 of such attributes are alsocreated and made to act in ways consistent with generating the assumedoutcome 106. Examples of synthetic contributors 204 are compositepersonalities, fictitious physical sensors and undocumented saleschannels. These contributors might be called upon respectively, to postfavorable opinions, to report balmy weather, or to describe sales to acertain population demographic as being “off the charts.” Thesesynthesized data 205 make up the history, or “necessary-past,” resultingin the outcome. The outcome occurs in the environment 201.

An outcome is the final state of sentiment, ranking and/or binaryresult, from all of those possible, in the future. Influence isinformation, action and/or expression of sentiment that determines anoutcome. Typically, historical data 102 is used to predict an outcomebased upon measurable attributes leading to a foreseeable state.Conversely, if an outcome is assumed or created, then specificattributes necessary, referred to herein after as a necessary-past, canbe generated synthetically and applied to affect the result.

Sheep are often influenced to an outcome by very few attributes. Onewell trained dog can herd several hundred sheep in a desired mannerusing three attributes, barking, nipping and position/motion. The herdcould easily overwhelm a single dog with only barking, nipping andrunning as attributes at his disposal. The dog learns from previousdata, which sheep to move first and the attribute most likely toinfluence the motion. Having moved very few of the herd, the dog thenrelies upon their influence upon each other to get the entire groupmoving in the desired direction, leaving him to only deal with outliers.The dog has created the future as opposed to forecasting it.

Supervised classification of data and patterns is used to identifyattributes that influence historical outcomes, in context. By example,if a Company has released a new candy bar and determines that theoutcome is that consumers like it, attributes will be determined fromanalysis of data from a different, but similar product release where theresult was that the product was liked. This modeling, used to determinethe influencing attributes, is done using best-known-methods in dataaggregation, mining and analysis.

A synthetic influencer, according to the invention, can effectivelycause a future occurrence by offering specific attributes necessary toexist prior to the outcome. FIG. 2 illustrates this process; a candymaker prefers that social media comments reflect that a new bar is wellliked and this is identified as the future occurrence, “bar is wellliked”. This well liked outcome is the desired outcome 106. Data 102 isgathered and used in FIG. 2 to show that several attributes arenecessary to achieve this outcome 106; using reaction to prior andcompeting products, a map of influencers is identified along with theircharacteristics, a relatively small number of whom were likely requiredto move a vast number of followers to an equivalent conclusion andexpression of sentiment. The algorithm then assigns synthetic membersfrom its database to express attribute values that are designed to movethe required volume of sentiment in the direction consistent with theassumed outcome.

Examples of practical applications for creation of the future bygenerating the necessary-past and contributing synthesized attributesinclude:

-   Successful product launch;-   Positive reputation;-   Increased awareness;-   Candidate election;-   Stock sentiment;-   Tourism increase;-   Web site traffic;-   Redirection of resources.

The core algorithms of a future creating inventions build a synthetichistory of attributes necessary for the achievement of the future state.Further, the algorithm creates and assigns influencers, based uponlearned characteristics, to implement the synthetic history 206. Inother words, for the future to have a specified, as opposed to anobserved or predicted, state, certain things must have occurred prior tothe outcome; the algorithm determines those things necessary to createthe future and directs the deployment thereof.

Creating the necessary-past 202 is done using available analysisalgorithms and techniques 103, 107 which are scored for the type of dataavailable. The attributes of the necessary-past are determined andscored or weighted as required by context and then shaped; such resultsare gained through the analysis of historical data 102 from physicalsensors, sentiment data from social media, collected sales/revenuefigures, voter preference, polls etc.

From the analysis above, algorithms are used to discover specificinfluencers along with the weighted effect of such influence 205.Characteristics such as demographics, persuasions, climate, top sellers,etc. are described. Synthetic influencers are then selected orconstructed to output data consistent with the future as cast. Theresult can be described as a “then-if” solution.

Finally, the invention directs the deployment of the syntheticinfluencers described above, ordered in time and adjusted in magnitude,as determined by internal algorithms 206. The outcome is tested andinfluence or influencers may or may not be modified or substituted.

Current prescriptive analytic techniques present “if-then” results. Suchresults rely upon the observed past to test the result of one ormultiple occurrences and determine a likely outcome.

There are four fundamental and multiple secondary components to thealgorithm:

-   The first basic component is a means and method to Extract,    Transform and Load (ETL) historical data 102 from disparate sources,    in terms of format, location, etc 201;-   The second component uses predictive analytic algorithms 101 to    generate models 103 from the data 102 and to identify attributes as    part of a necessary-past 202 of the outcome;-   The third component allows the future outcome to be entered in to    the algorithm and generates the values, in their native format, for    the attributes of a necessary-past 205;-   The fourth fundamental component of the algorithm identifies the    synthetic offerers from its internal database that will contribute    the attributes.

A major secondary component of the inventions is the database ofsynthetic contributors 204 whose characteristics result in very fewbeing required to create and demonstrate a necessary past. Othersecondary components of the invention include a means to build thesynthetic contributors using knowledge derived from examination of,assumed-real contributors identified as being necessary influencers inthe previously described historical models.

The inputs to the algorithm are therefore, the future as cast,historical data 102 collected that yielded a contextually similar resultand the output is a necessary-past, its attributes and the contributorsthereof.

Looking to FIG. 1 , a block diagram is shown for a traditional machinelearning predictive analytics application. To this application, theoutput creation features are added. In a traditional predictiveanalytics system, a training module 101 operates on a training dataset102 to create a machine learning model 103. The predictive analyticsapplication 105 calls the machine learning engine 104 with a specificset of event data. The machine learning engine 104 processes the eventdata through the machine learning model 103 to predict what result willoccur with the specific event data.

In the outcome creation portion of the system starts with the input ofthe desired outcome 106. Say a banker wants to increase the number ofshort term business loans. The system is asked to increase businessloans as the outcome. This outcome is sent to the outcome creationengine 107. The outcome creation engine 107 reverse engineers themachine learning model 103 to identify the feature drivers of the model103. Say the model 103 has rules that if a bank customer has a checkingaccount with the bank and uses the debit card and has a balance of lessthan $20,000 in the checking account, then the customer is likely to askfor a short term loan. The outcome creation engine 107 parses the rulesof the machine learning model 103, and returns checking account, debitcard, and balance less than $20,000.

The bank cannot control the amount of money in the account, so this isan uncontrolled feature 203. But the bank can influence the presence ofa checking account and the use of a debit card, perhaps by offeringdiscounts or increased advertising. So the output creation engine 107outputs 108 1) the presence of a checking account and 2) use of debitcard. In some embodiments, the balance less than $20,000 is also output108. The output 108 is the transfer of data to other software in someembodiments and in other embodiments the output 108 may be displayed ona screen, perhaps on the computer 301. In other embodiments, the output108 causes actions to be taken 109. In some embodiments, the server 303matches the required attributes 108 to a table of actions to take tocause the required attributes. For instance, this could include loweringthe price when increased sales are desired. Or buying products to causea price to increase. All of these actions 109 are automated within theserver.

Once the actions 109 are taken, the desired outcome 110 is effectuated.

FIG. 2 shows a broader view of embodiments of the present inventions.The environment 201 is the context upon which the machine learningoperates and upon which the algorithm seeks to impact with a desiredoutput 106. In our example above, the environment 201 is the bankingmarket. From the market environment 201, historical data 102 iscollected. This historical data 102 could include features such ascustomer name, address, the types of accounts and loans that thecustomer has, balances, etc. This historical data 102 is run through thetraining module 101 to build the machine learning model 103. The machinelearning model 103 and the desired outcome 106 are fed into the outcomecreation engine 107 to determine what is needed to generate the set ofnecessary-past attributes 202. Returning to our example, the machinelearning model 103 may determine that it uses the presence of a checkingaccount, the use of a debit card, and a balance of less than $20,000 topredict if a customer will request a short term loan. The outcomecreation engine 107 determines that checking account, debit card, andbalance less than $20,000 are needed to generate the set ofnecessary-past attributes 202. The set of necessary-past attributes 202looks to the two features lists, one list of synthetic features 204 andthe second list of uncontrolled features 203. The set of necessary-pastattributes 202 are filtered through the list of synthetic features 204.In some embodiments, these features lists 203, 204 are maintained by themachine using machine learning techniques. In other embodiments, theselists 203, 204 are entered by the banker or those setting up the system.

The determine synthetic contributors function 205 then take the featuresthat are controllable by the bank, in this case the checking account andof the debit card, and determines the required attributes. In this case,the algorithm looks for the presence of these features. In othersituations, it could be certain values of a feature. In someembodiments, the controllable features are tested to see if they have adeterminative effect, and are not overridden by the uncontrolledfeatures.

The list of desired attributes for the controllable features are theneither automatically sent to cause an action 206 or sent to a human forimplementation. In our example, the debit card could be automaticallysent out to existing customers with checking accounts and balances lessthan $20,000 to setup the attributes for the desired outcome. However,the opening of the checking account may require the bank to design andimplement an advertising campaign to bring in more checking accounts.Each of these actions 206 will impact the environment 201.

Because of the complexities of machine learning algorithms, specialpurpose computing may be needed to build and execute the machinelearning model described herein. FIG. 3 shows one such embodiment. Theuser enters the desired outcome 106 and perhaps views the output 108described here on a personal computing device such as a personalcomputer, laptop, tablet, smart phone, monitor, or similar device 301.The personal computing device 301 communicates through a network 302such as the Internet, a local area network, or perhaps through a directinterface to the server 303. The special purpose machine learning server303 is a high performance, multi-core computing device with significantstorage facilities 304 in order to store the machine learning trainingdata 102 for the model 103. Since this machine learning database 102 iscontinuously updated in some embodiments, this data must be kept onlineand accessible so that it can be updated. In addition, the real-timeediting of the model 103 as the user provides feedback to the model 103requires significant processing power to rebuild the model as feedbackis received. The server 303 is a high performance computing machineelectrically connected to the network 302 and to the storage facilities304.

Looking to FIG. 4 , a flowchart is shown creating a desired outcome. Theprocess begins by identifying the desired outcome 401. Knowing what isdesired, the process next looks through the data for an existing outcomein history 402. In this step 402, the exact occurrence is sought in thedata.

In the example in FIG. 5 , the desired outcome is to increase the numberof Real Time Payments made by a customer. In this example, we assumethat the typical customer does about 90% of their transactions asAutomated Clearing House (ACH) payments, typically for a nominal or nocost. About 10% of the payments are done with wire transfers, for asubstantial cost per transaction (maybe $25 per wire). A new paymentmethod called Real Time Payment (RTP) is introduced at a moderate price,perhaps $2 per transaction. The desired outcome 401 is to transitioncustomers from ACH to RTP payments. In FIG. 5 , a customer has beenidentified who converted a significant amount of their business from ACHto RTP. In one embodiment, this customer was found by the server 303 bysearching for customers who have more RTP payments than ACH payments. Insome embodiments, rather than a single customer an aggregate of aplurality of customers could be charted. In some embodiments thisaggregation is simply time based, and in other embodiments, the time isshifted to align the desired outcomes.

In the chart on FIG. 5 , the change from no RTP payments to many RTPpayments started in November or December 2019. So the server 303identifies the occurrence of the desired outcome 402 as December 2019,by comparing the RTP data point to see where a sharp increase occurs.

The next step in FIG. 4 is to create a data set 403 for a period of timeleading up to the desired change. This time period could be a parameterset by a user or it could be determined by repeatedly creating models404 and testing the results to see if an interesting result is produced.After the data set 403 is determined, the necessary part is modeled 404using traditional machine learning techniques used for predictiveanalytics.

In our example in FIG. 5 , the one year period is selected, and the dataset 403 is marked as from January 2019 to December 2019. A machinelearning model is run on the 2019 data, looking at the number of ACHpayments, the number of wire payments, and the number of RTP payments.The machine learning model 404 notices that in the six months before thedesired outcome on December 2019, the number of ACH payments decreasedand the number of wire payments increased. Essentially, the modelnotices that before the RTP payments were used by the customer, thecustomer started switching to a greater percentage of wire transfers for5-6 months, and then the customer moved to RTP payments.

In FIG. 4 , the process then identifies the actions to take to match theoutcome 405, effectuating the desired outcome 406.

In the example in FIG. 5 , the server 303 identifies that an increase inwire transfers will cause the customer to consider RTP as a method ofpayment. The server 303 then searches a list of possible actions for anaction that will increase wire transfers. For example, the server 303may determine that a significant sale on wire transfers may cause achange in the mix of wires and ACH payments. By significantlydiscounting wire transfers, perhaps to several dollars for a temporarysale, the customer switches over a number of payments from ACH to wire.Then when the wire transfer sale ends, the bank recommends that RTPpayments be used instead of wires. This will match the chart in FIG. 5for another customer.

The foregoing devices and operations, including their implementation,will be familiar to, and understood by, those having ordinary skill inthe art.

The above description of the embodiments, alternative embodiments, andspecific examples, are given by way of illustration and should not beviewed as limiting. Further, many changes and modifications within thescope of the present embodiments may be made without departing from thespirit thereof, and the present invention includes such changes andmodifications.

The invention claimed is:
 1. A method for identifying syntheticcontributors required to generate a desired outcome, the methodcomprising: creating a machine learning model by operating a trainingmodule on a machine learning database on a machine learning server;receiving the desired outcome at the machine learning server over anetwork from a computer; reverse engineering the machine learning modelby parsing rules in the machine learning model using the desired outcometo determine a set of necessary-past attributes for generating thedesired outcome, on the machine learning server, where thenecessary-past attributes are data and pattern attributes that need tobe present when the machine learning model is run to generate thedesired outcome; filtering the set of necessary-past attributes througha list of synthetic features to identify the synthetic contributorsrequired to generate the desired outcome, wherein the synthetic featuresare features input to the machine learning model that can be controlledand the synthetic contributors are presence or value attributes of themachine learning model that can be made to act in a way to generate thedesired outcome; and outputting the synthetic contributors.
 2. Themethod of claim 1 wherein the desired outcome relates to web sitetraffic.
 3. The method of claim 1 further comprising: generating thedesired outcome by automatically taking action to implement thesynthetic contributors.
 4. The method of claim 1 wherein the desiredoutcome relates to banking.
 5. The method of claim 4 wherein thesynthetic contributors include information about bank accounts.
 6. Themethod of claim 1 wherein the synthetic contributors are output to thecomputer.
 7. The method of claim 1 wherein the synthetic contributorsare output to software on the machine learning server.
 8. The method ofclaim 1 wherein the list of synthetic features is machine generated. 9.The method of claim 1 further comprising locating the desired outcome ina set of machine learning data and creating a dataset for the machinelearning model with data proximate to the desired outcome.
 10. A devicefor identifying synthetic contributors required to generate a desiredoutcome, the device comprising: a special purpose machine learningserver; a machine learning database electrically connected to thespecial purpose machine learning server; and a list of syntheticfeatures stored in the special purpose machine learning server, wherethe synthetic features are features input to a machine learning modelthat can be controlled; wherein the special purpose machine learningserver creates the machine learning model by operating a training moduleon the machine learning database on the machine learning server, acceptsan input of the desired outcome, and uses the desired outcome inconjunction with an outcome creation engine to reverse engineer themachine learning model by parsing rules of the machine learning modelusing the desired outcome to derive a set of necessary-past attributes,the set of necessary-past attributes filtered through the list ofsynthetic features to identify the synthetic contributors required togenerate the desired outcome, where the necessary-past attributes aredata and pattern attributes that need to be present when the machinelearning model is run to generate the desired outcome and the syntheticcontributors are presence or value attributes of the machine learningmodel that can be made to act in a way to generate the desired outcome.11. The device of claim 10 wherein the desired outcome relates to website traffic.
 12. The device of claim 10 wherein the desired outcome isgenerated by automatically taking action to implement the syntheticcontributors.
 13. The device of claim 10 wherein the desired outcomerelates to banking.
 14. The device of claim 13 wherein the syntheticcontributors includes information about bank accounts.
 15. The device ofclaim 10 wherein the synthetic contributors are shared with othersoftware on the special purpose machine learning server.
 16. The deviceof claim 10 wherein the synthetic contributors are output on a display.17. The device of claim 10 wherein the list of synthetic features ismachine generated.
 18. A device for generating a desired outcome, thedevice comprising: a special purpose machine learning server; and amachine learning database electrically connected to the special purposemachine learning server; wherein the special purpose machine learningserver comprises: a means for creating a machine learning model byoperating a training module on the machine learning database on themachine learning server; and a means for generating the desired outcomeusing the machine learning model.